Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| GARCH-MIDAS× | Компонентно-GARCH (Component GARCH)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2012 | 1999 |
| Създател≠ | Engle and Ghysels | Engle and Lee |
| Тип≠ | Time-varying variance model | Decomposed variance model |
| Основополагащ източник≠ | Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391. link ↗ | Engle, R. F., & Lee, G. (1999). A permanent and transitory component model of stock return volatility. Journal of Political Economy, 107(6), 1363-1384. link ↗ |
| Други названия | Mixed-frequency volatility model | Volatility components model |
| Свързани | 3 | 3 |
| Резюме≠ | GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by Engle and Ghysels (2012), this framework elegantly separates volatility time scales. The approach is powerful for understanding how macro conditions (growth, inflation) drive risk premia and for improved volatility forecasting. | Component GARCH decomposes conditional variance into transitory (short-term) and permanent (long-term) components with different dynamics, allowing flexibility in capturing volatility behavior at multiple frequencies. Introduced by Engle and Lee (1999), it elegantly models the empirical finding that volatility exhibits both rapid mean-reversion (daily shocks) and slow mean-reversion (level shifts). This framework is crucial for understanding volatility persistence and improving long-horizon volatility forecasting. |
| ScholarGateНабор от данни ↗ |
|
|